Modeling Demand
Module 7
Conceptual Structure of SIMQ Market Model
Firm Demand = Total Industry Demand * Share of Market
Firm Demand = Average Firm Demand * n * Share of Market
Firm Demand = Average Firm Demand * Normalized Share of Market
Macro-economic
Influences
Seasonal Patterns
Exogenous
Demand
Stage of Life Cycle
Industry Activity
Pricing, Promotion,
Endogenous
AFD
Demand
FD
Quality
Competitive Profile
Relative Pricing, Promotion,
Quality, and Loyalty
Relative
Demand
NSOM
Normalised Share of Market
NSOM = Firm Demand / Avg Firm Demand
Relative Price
(current)
Relative Advertising
(current, t-1, t-2 )
Pricing
NSOM
Promotion
Relative R&D
(t-1, t-2)
Loyalty
NSOM (t-1)
Quality
NSOM is firm specific and a measure of relative demand, the predictor
variables should also be relative to industry averages.
For example, relative price of the firm is
PREL = Firm’s Price / Industry Avg. Price
Calculating NSOM
11) Use a multiple regression to estimate NSOM.
Average Firm Demand
How many units will any firm sell on the average.
D
n
 Df
f 1
Error
n
Endog
Exog
AFD = Exogenous demand + Endogenous demand
Exogenous demand = “Base demand” X Seasonal effects
Endogenous demand = Influence of aggregate industry behavior
Average Firm Demand
AFD
Exogenous
Demand
Macro-Economic
Influences
Endogenous
Demand
Industry Behavior
- Pricing (Avg Price)
- Seasonality
- Promotion (Avg. Advertising)
- Stage of Life Cycle
- Product Quality (Avg. R&D)
Estimate Trend and
Seasonality using
Time Series Analysis
Estimate weights of these factors
using Regression Analysis
AFD = {(T*S) + (B0+B1*Avg P+..)}
AFD: Exogenous Demand
Base demand
Population, Income, Tastes, Product life cycle,
Substitutes and complements (Macro-economic
influences)
Seasonal demand
Weather, Customs, Holidays
Not all products are affected
Estimation is done using:
Time Series Decomposition
Calculating Exogenous AFD
1) Use Statpro to generate Seasonal Indices
Calculating Exogenous AFD
2) Use Seasonal Indices to De-seasonalize observations.
1438 / .895 = 1607
Calculating AFD
3) Fit a simple regression line to the de-seasonalized
observations.
Calculating AFD
4) Use the regression line to create a
‘de-seasonalized’ forecast.
11 * 65.213 + 1237.9 = 1955.2
Calculating AFD
5) Re-seasonalize the predicted forecast. This is the
exogenous portion of demand.
1.093 * 2150.9 = 2350.3
Calculating AFD
6) Calculate the residual error from the forecast.
This is the endogenous portion of demand.
2839 – 2350.3 = 488.7
Calculating AFD
7) Fit a multiple regression with the residuals as the
dependent variable and the firm data as the
independent variables.
Calculating AFD
8) Use the new regression equation to forecast
residuals. These are estimated of endogenous
demand.
15688.25 + (-43.885) * 371.9 + (.0032) * 97960 + (.0127) * 25610 = 4.2
Calculating AFD
9) Add the exogenous forecast to the endogenous
forecast to create a composite forecast.
2350.3 + 204.2 = 2554.6
Calculating AFD
10) Subtract the composite forecast from the
observations to find a total error.
2839 – 2554.6 = 284.4
Calculating AFD
11) Calculate the standard deviation of
the residuals.
Because there may be more explanation
that can be squeezed out of the trend
and attributed to the dependent
variables, repeat the process.
For subsequent iterations,
Raw AFD – Estimated Endogenous as
starting data for time series.
Continue till error stops decreasing.
Conceptual Structure of SIMQ Market Model
With Average Firm Demand and Normalized Share of Market
modeled, we can now create a decision support system for any
individual firm.
Exogenous
Demand
Endogenous
AFD
Demand
Relative
Demand
FD
NSOM
Example DSS